Sensing Suspension Behavior
Though Pat has done some nice digital modeling, the boss, Nole, is not yet satisfied. A core part of the digital twin concept is tracking the real-world item through its lifetime, and making constant comparisons between it and the digital model.
To make this possible, Pat mounts an accelerometer on each corner of the chassis, and hooks each one into the on-board control module. This module periodically streams data from the suspension and other critical systems to ALSET servers. Though Pat will be tracking acceleration, as opposed to position, it is possible to calculate position as the second integral of acceleration. Though this can get complicated when there are multiple axes of motion, it is relatively simple and reliable in one dimension. It should also be noted that the vibrational characteristics of the system are identical whether you are observing an acceleration graph or a position graph.
Before the design is set for production, the prototype car drives on a test track while streaming data from all the on-board sensors. Pat pays special attention to the "bumps" portion of the course. This portion sends the car over \(0.1m\), \(0.2m\), and \(0.3m\) bumps, which yields the following graph:
Because of design changes in other parts of the sedan, the results don't match Pat's model exactly. However, it is easy to use these data to infer a new model, so Pat does just that.
After a test production run, 10 cars are randomly sampled to drive on the test track, and their results are all nearly identical to the prototype.
Leveraging Twin Data Right Away
Five thousand cars are sold the first week after launch, and Pat receives suspension data from each of them. By running a large map-reduce job, Pat obtains the frequencies and damping ratios from many "bump" events for each of the 5,000 cars and plots the distributions:
At this scale, it can be seen that there is more variance than desired for some of the key performance constraints. There have been no customer complaints, but Pat notifies the production team, and the team puts measures in place to assemble the suspension with greater consistency.
Big Paybacks in the Long Run
A year later, some warranty claims begin trickling in. The main complaint is unwanted shaking/vibration. Pat looks at the data from some of the claims, and sees something like this:
As a seasoned suspension expert, Pat knows this extra vibration is probably due to a worn bushing or failure of some other rubber component, and alerts the repair technicians. Most repair technicians find that the upper spring seat is severely worn to the point of failure. The production team reports that the OEM supplier for this part has been flaky, and the due to high production demands, they have had to work with several backup suppliers and were unable to track which version of the part went into each car.
Total failure of this part could cause unpredictable vehicle handling, and Nole is on the verge of issuing a massive recall. This has huge financial implications, because replacing the part requires removing most of the suspension, and is very time consuming. Before the recall, Nole puts all hands toward finding a better solution.
While combing through the data of all the warranty claims, Pat realizes that there were signs of the imminent failure long before the customers reported a problem. Though the team can't directly identify cars with faulty parts, they can monitor the entire fleet and determine if something has gone awry.
Nole is relieved, and Pat is a hero. Pat works with an ALSET data scientist to finalize a predictive model, which works by comparing the real-world data with the theoretical suspension model. This leads to car owners being selectively notified of the pending part failure as needed, saving the company millions.
Pat's story has shown us a (simplified) real-world example of digital twin technology. In this example, a failure prediction algorithm was implemented reactively. But anomaly detection for failure prediction can be put in place preemptively if the use case warrants it. The key is that neither are possible if you aren't collecting data or do not have a computational model of the system (whether physics-based or learned via machine learning).
Though digital twin technology does have origins in rocket science, it's not that hard to understand, and even a simple model can be valuable. Of course, like anything, as the system gets more complex, or the requirements for model precision get higher, it can become a very difficult problem.
At Very, our data scientists and engineers are familiar with these problems and can work independently or with your engineers to develop a robust, scalable, digital twin solution.